Face recognition by neural network using bit-planes extracted from an image

An 8-bit digital image consists of 256 levels of gray-value and 8 layers of multilevel information of bits known as bit-plane information. A novel method utilizing higher order bit-plane information that contains majority of visually signi_cant data and dummy blank images as inputs to a multilayer feedforward Neural Network (NN) is proposed in this paper to perform face recognition. Experiments performed on the proposed face recognition model using two face databases, namely CMU AMP face expression database and Yale face database, show improvement in recognition rate compared to using only gray-level images as inputs to the NN

Generative adversarial networks in ophthalmology: what are these and how can they be used?

PURPOSE OF REVIEW: The development of deep learning (DL) systems requires a large amount of data, which may be limited by costs, protection of patient information and low prevalence of some conditions. Recent developments in artificial intelligence techniques have provided an innovative alternative to this challenge via the synthesis of biomedical images within a DL framework known as generative adversarial networks (GANs). This paper aims to introduce how GANs can be deployed for image synthesis in ophthalmology and to discuss the potential applications of GANs-produced images. RECENT FINDINGS: Image synthesis is the most relevant function of GANs to the medical field, and it has been widely used for generating 'new' medical images of various modalities. In ophthalmology, GANs have mainly been utilized for augmenting classification and predictive tasks, by synthesizing fundus images and optical coherence tomography images with and without pathologies such as age-related macular degeneration and diabetic retinopathy. Despite their ability to generate high-resolution images, the development of GANs remains data intensive, and there is a lack of consensus on how best to evaluate the outputs produced by GANs. SUMMARY: Although the problem of artificial biomedical data generation is of great interest, image synthesis by GANs represents an innovation with yet unclear relevance for ophthalmology

The Candida albicans transcription factor Cas5 couples stress responses, drug resistance and cell cycle regulation

We thank Cowen lab members for helpful discussions. We also thank David Rogers (University of Tennessee) for sharing microarray analysis of the CAS5 homozygous mutant, and Li Ang (University of Macau) for assistance in optimizing the ChIP-Seq experiments. J.L.X. is supported by a Canadian Institutes of Health Research Doctoral award and M.D.L. is supported by a Sir Henry Wellcome Postdoctoral Fellowship (Wellcome Trust 096072). B.T.G. holds an Ontario Graduate Scholarship. C.B. and B.J.A. are supported by the Canadian Institutes of Health Research Foundation Grants (FDN-143264 and -143265). D.J.K. is supported by a National Institute of Allergy and Infectious Diseases grant (1R01AI098450) and J.D.L.C.D. is supported by the University of Rochester School of Dentistry and Medicine PREP program (R25 GM064133). A.S. is supported by the Creighton University and the Nebraska Department of Health and Human Services (LB506-2017-55). K.H.W. is supported by the Science and Technology Development Fund of Macau S.A.R. (FDCT; 085/2014/A2). L.E.C. is supported by the Canadian Institutes of Health Research Operating Grants (MOP-86452 and MOP-119520), the Natural Sciences and Engineering Council (NSERC) of Canada Discovery Grants (06261 and 462167), and an NSERC E.W.R. Steacie Memorial Fellowship (477598).Peer reviewedPublisher PD

Towards Efficient Detection of Small Near-Earth Asteroids Using the Zwicky Transient Facility (ZTF)

We describe ZStreak, a semi-real-time pipeline specialized in detecting small, fast-moving near-Earth asteroids (NEAs) that is currently operating on the data from the newly-commissioned Zwicky Transient Facility (ZTF) survey. Based on a prototype originally developed by Waszczak et al. (2017) for the Palomar Transient Factory (PTF), the predecessor of ZTF, ZStreak features an improved machine-learning model that can cope with the $10\times$ data rate increment between PTF and ZTF. Since its first discovery on 2018 February 5 (2018 CL), ZTF/ZStreak has discovered $45$ confirmed new NEAs over a total of 232 observable nights until 2018 December 31. Most of the discoveries are small NEAs, with diameters less than $\sim100$ m. By analyzing the discovery circumstances, we find that objects having the first to last detection time interval under 2 hr are at risk of being lost. We will further improve real-time follow-up capabilities, and work on suppressing false positives using deep learning.Comment: PASP in pres

Inducing safer oblique trees without costs

Decision tree induction has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe or whether a person has a medical condition, the cost of misclassification in one of the classes is significantly higher than in the other class. Several authors have tackled this problem by developing cost-sensitive decision tree learning algorithms or have suggested ways of changing the distribution of training examples to bias the decision tree learning process so as to take account of costs. A prerequisite for applying such algorithms is the availability of costs of misclassification. Although this may be possible for some applications, obtaining reasonable estimates of costs of misclassification is not easy in the area of safety. This paper presents a new algorithm for applications where the cost of misclassifications cannot be quantified, although the cost of misclassification in one class is known to be significantly higher than in another class. The algorithm utilizes linear discriminant analysis to identify oblique relationships between continuous attributes and then carries out an appropriate modification to ensure that the resulting tree errs on the side of safety. The algorithm is evaluated with respect to one of the best known cost-sensitive algorithms (ICET), a well-known oblique decision tree algorithm (OC1) and an algorithm that utilizes robust linear programming

Functional Specialization of Cellulose Synthase Isoforms in a Moss Shows Parallels with Seed Plants

The secondary cell walls of tracheary elements and fibers are rich in cellulose microfibrils that are helically oriented and laterally aggregated. Support cells within the leaf midribs of mosses deposit cellulose-rich secondary cell walls, but their biosynthesis and microfibril organization have not been examined. Although the Cellulose Synthase (CESA) gene families of mosses and seed plants diversified independently, CESA knockout analysis in the moss Physcomitrella patens revealed parallels with Arabidopsis (Arabidopsis thaliana) in CESA functional specialization, with roles for both subfunctionalization and neofunctionalization. The similarities include regulatory uncoupling of the CESAs that synthesize primary and secondary cell walls, a requirement for two or more functionally distinct CESA isoforms for secondary cell wall synthesis, interchangeability of some primary and secondary CESAs, and some CESA redundancy. The cellulose-deficient midribs of ppcesa3/8 knockouts provided negative controls for the structural characterization of stereid secondary cell walls in wild type P. patens. Sum frequency generation spectra collected from midribs were consistent with cellulose microfibril aggregation, and polarization microscopy revealed helical microfibril orientation only in wild type leaves. Thus, stereid secondary walls are structurally distinct from primary cell walls, and they share structural characteristics with the secondary walls of tracheary elements and fibers. We propose a mechanism for the convergent evolution of secondary walls in which the deposition of aggregated and helically oriented microfibrils is coupled to rapid and highly localized cellulose synthesis enabled by regulatory uncoupling from primary wall synthesis

Predictors of Change in Functional Outcome at six months and twelve months after Severe Injury: A Retrospective Cohort Study

Background There is increasing focus on long-term survival, function and quality-of-life for trauma patients. There are few studies tracking longitudinal changes in functional outcome over time. The goal of our study was to compare the Glasgow Outcome Scale-Extended (GOSE) at 6 months and 12 months in blunt trauma survivors with an Injury Severity Score (ISS) of more than 15. Methods Using the Singapore National Trauma Registry 2011–2013, patients with 6-month GOSE and 12-month GOSE scores were analysed. Patients were grouped into three categories—those with the same score at 6 months and 12 months, an improvement in score, and a worse score at 12 months. Ordinal regression was used to identify risk factors for improved score. Patients with missing scores at either 6 months or 12 months were excluded. Results We identified 478 patients: 174 had an improvement in score, 233 stayed the same, and 71 had worse scores at 12 months compared to 6 months. On univariate ordinal regression, the following variables were associated with same or better function at 12-months compared to 6-months: male gender, being employed pre-injury, thoracic Abbreviated Injury Scale (AIS) of 3 or more, anatomical polytrauma (AIS of 3 or more in 2 or more body regions), and road traffic injury mechanism. Older age, low fall, increasing Charlson comorbidity scores, new injury severity score, and head and neck AIS of 3 or more were associated with worse function at 12 months compared to 6 months. ISS and revised trauma score were not significant predictors on univariate or multivariable analysis. On multivariable ordinal regression, motor vehicle mechanism (OR 2.78, 1.51–5.12, p = 0.001) was associated with improved function, while male gender (OR 1.36, 95% CI 1.02–1.82, p = 0.039) predicted improved function at 12 months. Conclusions Females experience worse functional outcomes at 12 months, potentially due to majority of female injuries being low falls in the elderly. In contrast, motor vehicle injury patients had better functional outcomes at 12 months. Additional interventional strategies for high-risk groups should be explored

Artificial intelligence and deep learning in ophthalmology

Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI 'black-box' algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward